Quantum Use Case #30: Payment settlement optimisation

Short answer: This use case involves applying quantum optimization algorithms to reorder high-value payment queues, aiming to reduce the liquidity required for gross settlement systems. It is currently a research-stage concept, with recent studies demonstrating potential efficiency gains in simulation and limited-scale applications, though widespread operational adoption remains distant due to hardware limitations.

The Problem: Liquidity in Gross Settlement

High-value payment systems (HVPSs) are typically liquidity intensive because payments are settled on a gross basis. This means that each payment is settled individually rather than in netted batches, requiring participants to hold significant liquidity to cover potential outflows at any given moment. State-of-the-art classical solutions to this problem include algorithms that seek netting sets and allow for ad hoc reordering of submitted payments. While these methods help, they do not explore the entire space of possible payment reorderings. The core challenge is that finding the optimal payment order among the entire space of reorderings is an NP-hard combinatorial optimization problem. As the number of payments in a batch increases, the computational complexity of finding the global optimum grows exponentially, making it intractable for classical computers to solve in real-time for large batches.

Quantum Approaches to Optimization

Researchers have proposed using quantum computing to address this combinatorial optimization challenge. Specifically, hybrid quantum annealing algorithms have been introduced to explore the entire space of payments reordering. The goal is to improve the liquidity efficiency of these systems without significantly increasing payment delays. By leveraging quantum effects, these algorithms aim to find better solutions to the NP-hard problem than classical heuristics can achieve.

One notable study introduced a new algorithm using a hybrid quantum annealing approach. The authors acknowledged the limitations in size and speed of today’s quantum hardware, which restricts the scale of problems that can be solved. Despite these constraints, the research demonstrated that the quantum approach could identify payment orders that reduce liquidity requirements compared to standard classical methods. This work was applied to Canada’s high-value payments system, showing that altering the payment queue to minimize liquidity requirements is feasible in a research context.

Application to Eurosystem Infrastructure

Further research has extended these techniques to other major payment systems. A study applied a similar technique to the Italian segment of TARGET2, the Eurosystem’s HVPS. This work utilized the Constrained Quadratic Model (CQM) solver provided by D-Wave. The application focused on optimizing payment batches of specific sizes, namely 70 and 140 payments. The results showed daily average liquidity savings of EUR 23 million for batches of size 70 and EUR 38 million for batches of size 140. These figures represent potential savings in a simulated or limited application context, highlighting the magnitude of the efficiency gains that could be realized if such optimizations were scaled appropriately.

It is important to note that these results are derived from research-stage applications. The use of specific hardware, such as the D-Wave CQM solver, indicates a collaboration between academic researchers and quantum hardware providers to test the viability of the approach. However, this does not imply that the technology is currently deployed in live production environments for daily settlement operations. The experiments serve to validate the theoretical premise that quantum optimization can outperform classical methods in this specific domain.

Current Limitations and Realism

The realism of this use case is constrained by the current state of quantum hardware. Quantum computers are still in an experimental phase. While they have the potential to solve certain complex problems of paramount interest in finance, they are not yet capable of handling the scale and speed required for real-time, high-volume payment settlement. The limitations in qubit count, coherence times, and error rates mean that today’s devices can only handle small problem instances, such as the batch sizes of 70 or 140 mentioned in the research.

Moreover, the integration of quantum solvers into existing financial infrastructure presents significant engineering and regulatory challenges. Payment systems require extreme reliability, low latency, and strict compliance with operational standards. Introducing a quantum component into the critical path of settlement would require rigorous validation and likely a hybrid architecture where quantum solutions are used as advisory inputs or for offline optimization rather than real-time decision-making. Therefore, while the research shows promising results in simulation and limited-scale tests, the transition to operational adoption is a long-term prospect.

Broader Financial Implications

Beyond payment settlement, quantum optimization has broader implications for the financial system. Quantum computers may have a profound impact on finance by providing faster and potentially more accurate solutions to complex problems. For instance, quantum simulation algorithms can be leveraged in testing and macroeconomic analysis, while quantum optimization can be used in asset pricing. The ability to solve NP-hard problems more efficiently could reshape risk management, portfolio optimization, and systemic stability analysis.

However, the advent of quantum computers also introduces potential risks. These risks mean the opportunities and the vulnerabilities of quantum computing both need to be understood. For payment settlement, the primary opportunity is liquidity efficiency, but the risks include dependency on emerging technology and the potential for new types of systemic failure if quantum components are integrated without adequate safeguards.

FAQ

Is quantum payment settlement optimization currently in use?

No. Most use cases are research-stage. While studies have shown improvements in simulation and limited-scale applications, such as optimizing batches of 70 or 140 payments, there is no evidence of widespread commercial or operational deployment in live high-value payment systems.

What specific hardware was used in the research?

One study utilized the Constrained Quadratic Model (CQM) solver provided by D-Wave to apply quantum reordering techniques to the Italian segment of TARGET2. Other research has employed hybrid quantum annealing algorithms. These are experimental setups used to validate the theoretical approach, not indicators of current production infrastructure.

How much liquidity can be saved?

In a study applying quantum reordering to TARGET2 batches, researchers observed daily average liquidity savings of EUR 23 million for batches of size 70 and EUR 38 million for batches of size 140. These figures are derived from research applications and should not be extrapolated to full-scale operational savings without further validation.

Why is this problem difficult for classical computers?

Finding the optimal payment order among the entire space of reorderings is an NP-hard combinatorial optimization problem. As the number of payments increases, the number of possible reorderings grows factorially, making it computationally intractable for classical algorithms to find the global optimum in real-time.

What are the main barriers to adoption?

The primary barriers are the limitations in size and speed of today’s quantum hardware. Current devices cannot handle the scale and latency requirements of real-time high-volume payment settlement. Additionally, integrating quantum components into critical financial infrastructure requires rigorous validation and regulatory approval, which are ongoing areas of research and policy discussion.